用贝叶斯方法稀疏扫描快速重建中红外光声图像，实现无需标记的快速病理诊断。
Bayesian reconstruction of sparse raster-scanned mid-infrared optoacoustic signals enables fast, label-free chemical microscopy
- 基于光学机械模型和样本先验知识构建前向模型，实现无训练数据的图像重建。
- 采集速度提升10倍，结构相似性（SSIM）超0.93，接近全扫描质量。
- 适合需要快速病理分析的临床场景，如术中组织评估。
高光谱光声显微镜（OAM）可提供无标记的生物分子对比成像，具有作为新鲜切除组织快速诊断工具的巨大潜力。然而，当前耗时的光栅扫描成像限制了其在临床中的应用，例如在术中病理评估中需几分钟内完成组织显微图像获取。本文提出一种非数据驱动的计算框架——贝叶斯稀疏扫描光声显微镜（BayROM），通过稀疏数据采集与基于模型的图像重建，实现快速成像。不同于传统机器学习，BayROM不依赖训练数据，而是利用1）光机系统特性建立前向模型，2）样本先验知识，从稀疏数据中重构图像。实验表明，BayROM使成像速度提升十倍，结构相似性（SSIM）超过0.93，显著推动了OAM在快速、无标记术中病理学中的临床转化。
原文摘要 · Abstract (English)
Hyperspectral optoacoustic microscopy (OAM) enables obtaining images with label-free biomolecular contrast, offering excellent perspectives as a diagnostic tool to assess freshly excised and unprocessed tissues. However, time-consuming raster-scanning image formation currently limits the translation potential of OAM into the clinical setting-for instance, in intraoperative histopathological assessments-where micrographs of excised tissue need to be taken within a few minutes for fast clinical decision-making. Here, we present a non-data-driven computational framework tailored to enable fast OAM by sparse data acquisition and model-based image reconstruction, termed Bayesian raster-computed optoacoustic microscopy (BayROM). Unlike conventional machine learning, BayROM doesn't require training datasets, but instead, it employs 1) optomechanical system properties to define a forward model and 2) prior knowledge of the imaged samples to facilitate reconstructing images based on the sparsely acquired data. We show that BayROM enables acquiring micrographs ten times faster and with structural similarity (SSIM) indices greater than 0.93 compared to conventional raster scanning microscopy, thus facilitating the clinical translation of OAM for fast, label-free intraoperative histopathology.
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